install.packages("wesanderson")
## Installing package into '/home/rstudio-user/R/x86_64-pc-linux-gnu-library/4.0'
## (as 'lib' is unspecified)
library(wesanderson)
install.packages("maps")
## Installing package into '/home/rstudio-user/R/x86_64-pc-linux-gnu-library/4.0'
## (as 'lib' is unspecified)
install.packages("mapdata")
## Installing package into '/home/rstudio-user/R/x86_64-pc-linux-gnu-library/4.0'
## (as 'lib' is unspecified)
install.packages("viridis")
## Installing package into '/home/rstudio-user/R/x86_64-pc-linux-gnu-library/4.0'
## (as 'lib' is unspecified)
library(maps)
library(mapdata)
library(tidyverse)
## ── Attaching packages ────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──
## ✓ ggplot2 3.3.2     ✓ purrr   0.3.4
## ✓ tibble  3.0.3     ✓ dplyr   1.0.2
## ✓ tidyr   1.1.2     ✓ stringr 1.4.0
## ✓ readr   1.3.1     ✓ forcats 0.5.0
## ── Conflicts ───────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
## x purrr::map()    masks maps::map()
library(lubridate)
## 
## Attaching package: 'lubridate'
## The following objects are masked from 'package:base':
## 
##     date, intersect, setdiff, union
library(viridis)
## Loading required package: viridisLite
daily_report <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/04-02-2020.csv")) %>% 
  rename(Long = "Long_") 
## Parsed with column specification:
## cols(
##   FIPS = col_double(),
##   Admin2 = col_character(),
##   Province_State = col_character(),
##   Country_Region = col_character(),
##   Last_Update = col_character(),
##   Lat = col_double(),
##   Long_ = col_double(),
##   Confirmed = col_double(),
##   Deaths = col_double(),
##   Recovered = col_double(),
##   Active = col_double(),
##   Combined_Key = col_character()
## )
ggplot(daily_report, aes(x = Long, y = Lat, size = Confirmed/1000)) +
    borders("world", colour = NA, fill = "grey90") +
    theme_bw() +
    geom_point(shape = 21, color='purple', fill='purple', alpha = 0.5) +
    labs(title = 'World COVID-19 Confirmed cases',x = '', y = '',
        size="Cases (x1000))") +
    theme(legend.position = "right") +
    coord_fixed(ratio=1.5)
## Warning: Removed 54 rows containing missing values (geom_point).

daily_report <-   read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/04-05-2020.csv")) %>% 
  rename(Long = "Long_") %>% 
  filter(Country_Region == "US") %>% 
  filter (!Province_State %in% c("Alaska","Hawaii", "American Samoa",
                  "Puerto Rico","Northern Mariana Islands", 
                  "Virgin Islands", "Recovered", "Guam", "Grand Princess",
                  "District of Columbia", "Diamond Princess")) %>% 
  filter(Lat > 0)
## Parsed with column specification:
## cols(
##   FIPS = col_character(),
##   Admin2 = col_character(),
##   Province_State = col_character(),
##   Country_Region = col_character(),
##   Last_Update = col_datetime(format = ""),
##   Lat = col_double(),
##   Long_ = col_double(),
##   Confirmed = col_double(),
##   Deaths = col_double(),
##   Recovered = col_double(),
##   Active = col_double(),
##   Combined_Key = col_character()
## )
ggplot(daily_report, aes(x = Long, y = Lat, size = Confirmed)) +
    borders("state", colour = "black", fill = "grey90") +
    theme_bw() +
    geom_point(shape = 21, color='purple', fill='purple', alpha = 0.5) +
    labs(title = 'COVID-19 Confirmed Cases in the US', x = '', y = '',
        size="Cases") +
    theme(legend.position = "right") +
    coord_fixed(ratio=1.5)

Mapping data to shapes

daily_report <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/04-02-2020.csv")) %>% 
  rename(Long = "Long_") %>% 
  filter(Country_Region == "US") %>% 
  group_by(Province_State) %>% 
  summarize(Confirmed = sum(Confirmed)) %>% 
  mutate(Province_State = tolower(Province_State))
## Parsed with column specification:
## cols(
##   FIPS = col_double(),
##   Admin2 = col_character(),
##   Province_State = col_character(),
##   Country_Region = col_character(),
##   Last_Update = col_character(),
##   Lat = col_double(),
##   Long_ = col_double(),
##   Confirmed = col_double(),
##   Deaths = col_double(),
##   Recovered = col_double(),
##   Active = col_double(),
##   Combined_Key = col_character()
## )
## `summarise()` ungrouping output (override with `.groups` argument)

load the US map data

us <- map_data("state")

We need to join the us map data with our daily report to make one data frame/tibble

state_join <- left_join(us, daily_report, by = c("region" = "Province_State"))

PLot State Map

ggplot(data = us, mapping = aes(x = long, y = lat, group = group)) + 
  coord_fixed(1.3) + 
  geom_polygon(data = state_join, aes(fill = Confirmed), color = "black") +
  scale_fill_gradientn(colours = 
                         wes_palette("Zissou1", 100, type = "continuous"),
                         trans = "log10") +
  labs(title = "COVID-19 Confirmed Cases in the US'")

RColorBrewer

library(RColorBrewer)

Get and format the covid report data

report_03_27_2020 <-   read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/04-02-2020.csv")) %>% 
  rename(Long = "Long_") %>% 
  unite(Key, Admin2, Province_State, sep = ".") %>% 
  group_by(Key) %>% 
  summarize(Confirmed = sum(Confirmed)) %>% 
  mutate(Key = tolower(Key))
## Parsed with column specification:
## cols(
##   FIPS = col_double(),
##   Admin2 = col_character(),
##   Province_State = col_character(),
##   Country_Region = col_character(),
##   Last_Update = col_character(),
##   Lat = col_double(),
##   Long_ = col_double(),
##   Confirmed = col_double(),
##   Deaths = col_double(),
##   Recovered = col_double(),
##   Active = col_double(),
##   Combined_Key = col_character()
## )
## `summarise()` ungrouping output (override with `.groups` argument)

get and format the map data

us <- map_data("state")
counties <- map_data("county") %>% 
  unite(Key, subregion, region, sep = ".", remove = FALSE)

Join the 2 tibbles

state_join <- left_join(counties, report_03_27_2020, by = c("Key"))
ggplot(data = us, mapping = aes(x = long, y = lat, group = group)) + 
  coord_fixed(1.3) +
  borders("state", colour = "black") +
  geom_polygon(data = state_join, aes(fill = Confirmed)) +
  scale_fill_gradientn(colors = brewer.pal(n = 5, name = "PuRd"),
                       breaks = c(1, 10, 100, 1000, 10000, 100000),
                       trans = "log10", na.value = "White") +
  ggtitle("Number of Confirmed Cases by US County") +
  theme_bw() 
## Warning: Transformation introduced infinite values in discrete y-axis

daily_report <-   read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/04-02-2020.csv")) %>% 
  rename(Long = "Long_") %>% 
  filter(Province_State == "Massachusetts") %>% 
  group_by(Admin2) %>% 
  summarize(Confirmed = sum(Confirmed)) %>% 
  mutate(Admin2 = tolower(Admin2))
## Parsed with column specification:
## cols(
##   FIPS = col_double(),
##   Admin2 = col_character(),
##   Province_State = col_character(),
##   Country_Region = col_character(),
##   Last_Update = col_character(),
##   Lat = col_double(),
##   Long_ = col_double(),
##   Confirmed = col_double(),
##   Deaths = col_double(),
##   Recovered = col_double(),
##   Active = col_double(),
##   Combined_Key = col_character()
## )
## `summarise()` ungrouping output (override with `.groups` argument)
us <- map_data("state")
ma_us <- subset(us, region == "massachusetts")
counties <- map_data("county")
ma_county <- subset(counties, region == "massachusetts")
state_join <- left_join(ma_county, daily_report, by = c("subregion" = "Admin2")) 

plot state map

ggplot(data = ma_county, mapping = aes(x = long, y = lat, group = group)) + 
  coord_fixed(1.3) +
  geom_polygon(data = state_join, aes(fill = Confirmed), color = "white") +
    scale_fill_gradientn(colors = brewer.pal(n = 5, name = "BuGn"),
                         trans = "log10") +
  labs(title = "COVID-19 Confirmed Cases in Massachusetts'")

daily_report
## # A tibble: 14 x 2
##    Admin2              Confirmed
##    <chr>                   <dbl>
##  1 barnstable                283
##  2 berkshire                 213
##  3 bristol                   424
##  4 dukes and nantucket        12
##  5 essex                    1039
##  6 franklin                   85
##  7 hampden                   546
##  8 hampshire                 102
##  9 middlesex                1870
## 10 norfolk                   938
## 11 plymouth                  621
## 12 suffolk                  1896
## 13 unassigned                270
## 14 worcester                 667

Interactive Graphs

library(plotly)
## 
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## The following object is masked from 'package:stats':
## 
##     filter
## The following object is masked from 'package:graphics':
## 
##     layout
ggplotly(
  ggplot(data = ma_county, mapping = aes(x = long, y = lat, group = group)) + 
  coord_fixed(1.3) + 
# Add data layer
  geom_polygon(data = state_join, aes(fill = Confirmed), color = "black") +
    scale_fill_gradientn(colours = 
                         wes_palette("Zissou1", 100, type = "continuous")) +
  ggtitle("COVID-19 Cases in MA") +
# Cleaning up the graph
  labs(x=NULL, y=NULL) +
  theme(panel.border = element_blank()) +
  theme(panel.background = element_blank()) +
  theme(axis.ticks = element_blank()) +
  theme(axis.text = element_blank())
)
## Warning: `group_by_()` is deprecated as of dplyr 0.7.0.
## Please use `group_by()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.
# Read in the daily report
daily_report <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/09-26-2020.csv")) %>% 
  rename(Long = "Long_") %>% 
  group_by(Country_Region) %>% 
  summarize(Confirmed = sum(Confirmed), Deaths = sum(Deaths))
## Parsed with column specification:
## cols(
##   FIPS = col_double(),
##   Admin2 = col_character(),
##   Province_State = col_character(),
##   Country_Region = col_character(),
##   Last_Update = col_datetime(format = ""),
##   Lat = col_double(),
##   Long_ = col_double(),
##   Confirmed = col_double(),
##   Deaths = col_double(),
##   Recovered = col_double(),
##   Active = col_double(),
##   Combined_Key = col_character(),
##   Incidence_Rate = col_double(),
##   `Case-Fatality_Ratio` = col_double()
## )
## `summarise()` ungrouping output (override with `.groups` argument)
# Read in the world map data
world <- as_tibble(map_data("world"))

# Check to see if there are differences in the naming of countries
setdiff(world$region, daily_report$Country_Region) 
##  [1] "Aruba"                               "Anguilla"                           
##  [3] "American Samoa"                      "Antarctica"                         
##  [5] "French Southern and Antarctic Lands" "Antigua"                            
##  [7] "Barbuda"                             "Saint Barthelemy"                   
##  [9] "Bermuda"                             "Ivory Coast"                        
## [11] "Democratic Republic of the Congo"    "Republic of Congo"                  
## [13] "Cook Islands"                        "Cape Verde"                         
## [15] "Curacao"                             "Cayman Islands"                     
## [17] "Czech Republic"                      "Canary Islands"                     
## [19] "Falkland Islands"                    "Reunion"                            
## [21] "Mayotte"                             "French Guiana"                      
## [23] "Martinique"                          "Guadeloupe"                         
## [25] "Faroe Islands"                       "Micronesia"                         
## [27] "UK"                                  "Guernsey"                           
## [29] "Greenland"                           "Guam"                               
## [31] "Heard Island"                        "Isle of Man"                        
## [33] "Cocos Islands"                       "Christmas Island"                   
## [35] "Chagos Archipelago"                  "Jersey"                             
## [37] "Siachen Glacier"                     "Kiribati"                           
## [39] "Nevis"                               "Saint Kitts"                        
## [41] "South Korea"                         "Saint Martin"                       
## [43] "Marshall Islands"                    "Macedonia"                          
## [45] "Myanmar"                             "Northern Mariana Islands"           
## [47] "Montserrat"                          "New Caledonia"                      
## [49] "Norfolk Island"                      "Niue"                               
## [51] "Bonaire"                             "Sint Eustatius"                     
## [53] "Saba"                                "Nauru"                              
## [55] "Pitcairn Islands"                    "Palau"                              
## [57] "Puerto Rico"                         "North Korea"                        
## [59] "Madeira Islands"                     "Azores"                             
## [61] "Palestine"                           "French Polynesia"                   
## [63] "South Sandwich Islands"              "South Georgia"                      
## [65] "Saint Helena"                        "Ascension Island"                   
## [67] "Solomon Islands"                     "Saint Pierre and Miquelon"          
## [69] "Swaziland"                           "Sint Maarten"                       
## [71] "Turks and Caicos Islands"            "Turkmenistan"                       
## [73] "Tonga"                               "Trinidad"                           
## [75] "Tobago"                              "Taiwan"                             
## [77] "USA"                                 "Vatican"                            
## [79] "Grenadines"                          "Saint Vincent"                      
## [81] "Virgin Islands"                      "Vanuatu"                            
## [83] "Wallis and Futuna"                   "Samoa"
world <- as_tibble(map_data("world")) %>% 
 mutate(region = str_replace_all(region, c("USA" = "US", "Czech Republic" = "Czechia",  
        "Ivory Coast" = "Cote d'Ivoire", "Democratic Republic of the Congo" = "Congo (Kinshasa)", 
        "Republic of Congo" = "Congo (Brazzaville)"))) 

# Join the covid report with the map data
country_join <- left_join(world, daily_report, by = c("region" = "Country_Region"))

# Create the graph
ggplotly(
ggplot(data = world, mapping = aes(x = long, y = lat, text = region, group = group)) + 
  coord_fixed(1.3) + 
# Add data layer
  geom_polygon(data = country_join, aes(fill = Deaths), color = "black") +
  scale_fill_gradientn(colours = 
                         wes_palette("Zissou1", 100, type = "continuous")) +
  labs(title = "COVID-19 Deaths")
) 

Exercise 1

# Read in the daily report
daily_report <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/09-26-2020.csv")) %>% 
  rename(Long = "Long_") %>% 
  group_by(Country_Region) %>% 
  summarize(Lat = median(Lat), Long = median(Long)) %>% 
  filter(Lat != "NA") %>%
  filter(Long != "NA")
## Parsed with column specification:
## cols(
##   FIPS = col_double(),
##   Admin2 = col_character(),
##   Province_State = col_character(),
##   Country_Region = col_character(),
##   Last_Update = col_datetime(format = ""),
##   Lat = col_double(),
##   Long_ = col_double(),
##   Confirmed = col_double(),
##   Deaths = col_double(),
##   Recovered = col_double(),
##   Active = col_double(),
##   Combined_Key = col_character(),
##   Incidence_Rate = col_double(),
##   `Case-Fatality_Ratio` = col_double()
## )
## `summarise()` ungrouping output (override with `.groups` argument)
# Read in the world map data
world <- as_tibble(map_data("world"))

# Check to see if there are differences in the naming of countries
setdiff(world$region, daily_report$Country_Region) 
##  [1] "Aruba"                               "Anguilla"                           
##  [3] "American Samoa"                      "Antarctica"                         
##  [5] "French Southern and Antarctic Lands" "Antigua"                            
##  [7] "Barbuda"                             "Saint Barthelemy"                   
##  [9] "Bermuda"                             "Canada"                             
## [11] "Chile"                               "Ivory Coast"                        
## [13] "Democratic Republic of the Congo"    "Republic of Congo"                  
## [15] "Cook Islands"                        "Colombia"                           
## [17] "Cape Verde"                          "Curacao"                            
## [19] "Cayman Islands"                      "Czech Republic"                     
## [21] "Germany"                             "Canary Islands"                     
## [23] "Spain"                               "Falkland Islands"                   
## [25] "Reunion"                             "Mayotte"                            
## [27] "French Guiana"                       "Martinique"                         
## [29] "Guadeloupe"                          "Faroe Islands"                      
## [31] "Micronesia"                          "UK"                                 
## [33] "Guernsey"                            "Greenland"                          
## [35] "Guam"                                "Heard Island"                       
## [37] "Isle of Man"                         "India"                              
## [39] "Cocos Islands"                       "Christmas Island"                   
## [41] "Chagos Archipelago"                  "Jersey"                             
## [43] "Japan"                               "Siachen Glacier"                    
## [45] "Kiribati"                            "Nevis"                              
## [47] "Saint Kitts"                         "South Korea"                        
## [49] "Saint Martin"                        "Marshall Islands"                   
## [51] "Macedonia"                           "Myanmar"                            
## [53] "Northern Mariana Islands"            "Montserrat"                         
## [55] "New Caledonia"                       "Norfolk Island"                     
## [57] "Niue"                                "Bonaire"                            
## [59] "Sint Eustatius"                      "Saba"                               
## [61] "Netherlands"                         "Nauru"                              
## [63] "Pitcairn Islands"                    "Peru"                               
## [65] "Palau"                               "Puerto Rico"                        
## [67] "North Korea"                         "Madeira Islands"                    
## [69] "Azores"                              "Palestine"                          
## [71] "French Polynesia"                    "South Sandwich Islands"             
## [73] "South Georgia"                       "Saint Helena"                       
## [75] "Ascension Island"                    "Solomon Islands"                    
## [77] "Saint Pierre and Miquelon"           "Swaziland"                          
## [79] "Sint Maarten"                        "Turks and Caicos Islands"           
## [81] "Turkmenistan"                        "Tonga"                              
## [83] "Trinidad"                            "Tobago"                             
## [85] "Taiwan"                              "USA"                                
## [87] "Vatican"                             "Grenadines"                         
## [89] "Saint Vincent"                       "Virgin Islands"                     
## [91] "Vanuatu"                             "Wallis and Futuna"                  
## [93] "Samoa"
world <- as_tibble(map_data("world")) %>% 
 mutate(region = str_replace_all(region, c("USA" = "US", "Czech Republic" = "Czechia",  
        "Ivory Coast" = "Cote d'Ivoire", "Democratic Republic of the Congo" = "Congo (Kinshasa)", 
        "Republic of Congo" = "Congo (Brazzaville)")))

# Join the covid report with the map data
country_join <- left_join(world, daily_report, by = c("region" = "Country_Region"))

# Create the graph
ggplotly(
ggplot(data = world, mapping = aes(x = long, y = lat, text = region, group = group)) + 
  coord_fixed(1.3) + 
# Add data layer
  geom_polygon(data = country_join, aes(fill = Long), color = "black") +
  scale_fill_gradientn(colours = 
                         wes_palette("Zissou1", 100, type = "continuous")) +
  labs(title = "COVID-19 Deaths")
)

Exercise 2

daily_report <-   read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/09-26-2020.csv")) %>% 
  rename(Long = "Long_") %>% 
  filter(Country_Region == "US") %>% 
  filter (!Province_State %in% c("Alaska","Hawaii", "American Samoa",
                  "Puerto Rico","Northern Mariana Islands", 
                  "Virgin Islands", "Recovered", "Guam", "Grand Princess",
                  "District of Columbia", "Diamond Princess")) %>% 
  filter(Lat > 0)
## Parsed with column specification:
## cols(
##   FIPS = col_double(),
##   Admin2 = col_character(),
##   Province_State = col_character(),
##   Country_Region = col_character(),
##   Last_Update = col_datetime(format = ""),
##   Lat = col_double(),
##   Long_ = col_double(),
##   Confirmed = col_double(),
##   Deaths = col_double(),
##   Recovered = col_double(),
##   Active = col_double(),
##   Combined_Key = col_character(),
##   Incidence_Rate = col_double(),
##   `Case-Fatality_Ratio` = col_double()
## )
ggplot(daily_report, aes(x = Long, y = Lat, size = Confirmed)) +
    borders("state", colour = "black", fill = "grey90") +
    theme_bw() +
    geom_point(shape = 21, color='purple', fill='purple', alpha = 0.5) +
    labs(title = 'COVID-19 Confirmed Cases in the US', x = '', y = '',
        size="Cases") +
    theme(legend.position = "right") +
    coord_fixed(ratio=1.5)

mybreaks <- c(1, 100, 1000, 10000, 10000)
ggplot(daily_report, aes(x = Long, y = Lat, size = Confirmed)) +
    borders("state", colour = "white", fill = "grey90") +
    geom_point(aes(x=Long, y=Lat, size=Confirmed, color=Confirmed),stroke=F, alpha=0.7) +
    scale_size_continuous(name="Cases", trans="log", range=c(1,7), 
                        breaks=mybreaks, labels = c("1-99",
                        "100-999", "1,000-9,999", "10,000-99,999", "50,000+")) +
    scale_color_viridis_c(option="viridis",name="Cases", trans= "log", breaks=mybreaks, labels = c("1-99",
                        "100-999", "1,000-9,999", "10,000-99,999", "50,000+"))  +
# Cleaning up the graph
  
  theme_void() + 
    guides( colour = guide_legend()) +
    labs(title = "Anisa Dhana's lagout for COVID-19 Confirmed Cases in the US'") +
    theme(
      legend.position = "bottom",
      text = element_text(color = "#22211d"),
      plot.background = element_rect(fill = "#ffffff", color = NA), 
      panel.background = element_rect(fill = "#ffffff", color = NA), 
      legend.background = element_rect(fill = "#ffffff", color = NA)
    ) +
    coord_fixed(ratio=1.5)
## Warning: Transformation introduced infinite values in discrete y-axis

## Warning: Transformation introduced infinite values in discrete y-axis
## Warning in sqrt(x): NaNs produced
## Warning: Removed 6 rows containing missing values (geom_point).

Exercise 3

report_09_26_2020 <-   read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/09-26-2020.csv")) %>% 
  rename(Long = "Long_") %>% 
  unite(Key, Admin2, Province_State, sep = ".") %>% 
  group_by(Key) %>% 
  summarize(Confirmed = sum(Confirmed)) %>% 
  mutate(Key = tolower(Key))
## Parsed with column specification:
## cols(
##   FIPS = col_double(),
##   Admin2 = col_character(),
##   Province_State = col_character(),
##   Country_Region = col_character(),
##   Last_Update = col_datetime(format = ""),
##   Lat = col_double(),
##   Long_ = col_double(),
##   Confirmed = col_double(),
##   Deaths = col_double(),
##   Recovered = col_double(),
##   Active = col_double(),
##   Combined_Key = col_character(),
##   Incidence_Rate = col_double(),
##   `Case-Fatality_Ratio` = col_double()
## )
## `summarise()` ungrouping output (override with `.groups` argument)
# get and format the map data
us <- map_data("state")
counties <- map_data("county") %>% 
  unite(Key, subregion, region, sep = ".", remove = FALSE)
# Join the 2 tibbles
state_join <- left_join(counties, report_09_26_2020, by = c("Key"))
# sum(is.na(state_join$Confirmed))
ggplot(data = us, mapping = aes(x = long, y = lat, group = group)) + 
  coord_fixed(1.3) + 
  # Add data layer
  borders("state", colour = "black") +
  geom_polygon(data = state_join, aes(fill = Confirmed)) +
  scale_fill_gradientn(colors = brewer.pal(n = 5, name = "PuBu"),
                       breaks = c(1, 10, 100, 1000, 10000, 100000),
                       trans = "log10", na.value = "White") +
  ggtitle("Number of Confirmed Cases by US County") +
  theme_bw() 
## Warning: Transformation introduced infinite values in discrete y-axis


## Exercise 4


```r
library(plotly)
daily_report <-   read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/09-26-2020.csv")) %>% 
  rename(Long = "Long_") %>% 
  filter(Province_State == "California") %>% 
  group_by(Admin2) %>% 
  summarize(Confirmed = sum(Confirmed)) %>% 
  mutate(Admin2 = tolower(Admin2))
## Parsed with column specification:
## cols(
##   FIPS = col_double(),
##   Admin2 = col_character(),
##   Province_State = col_character(),
##   Country_Region = col_character(),
##   Last_Update = col_datetime(format = ""),
##   Lat = col_double(),
##   Long_ = col_double(),
##   Confirmed = col_double(),
##   Deaths = col_double(),
##   Recovered = col_double(),
##   Active = col_double(),
##   Combined_Key = col_character(),
##   Incidence_Rate = col_double(),
##   `Case-Fatality_Ratio` = col_double()
## )
## `summarise()` ungrouping output (override with `.groups` argument)
us <- map_data("state")
ca_us <- subset(us, region == "california")
counties <- map_data("county")
ca_county <- subset(counties, region == "california")
state_join <- left_join(ca_county, daily_report, by = c("subregion" = "Admin2")) 

library(wesanderson)
ggplotly(
  ggplot(data = ca_county, mapping = aes(x = long, y = lat, group = group)) + 
  coord_fixed(1.3) + 
# Add data layer
  geom_polygon(data = state_join, aes(fill = Confirmed), color = "black") +
    scale_fill_gradientn(colours = 
                         wes_palette("Zissou1", 100, type = "continuous")) +
  ggtitle("COVID-19 Cases in CA") +
# Cleaning up the graph
  labs(x=NULL, y=NULL) +
  theme(panel.border = element_blank()) +
  theme(panel.background = element_blank()) +
  theme(axis.ticks = element_blank()) +
  theme(axis.text = element_blank())
)